The present embodiments relate to the control of a turbine (e.g., a gas turbine or a wind turbine).
A turbine may assume a large number of successive different states during operation. In order to sense these different states, many different sensors are conventionally used to provide sensor values ascertained on the turbine.
On account of the resultant complexity, many known control methods for turbines use neural networks.
An example of such a known method for controlling a turbine using a neural network is described in the patent specification DE 10 2007 001 025 B4.
The control of a turbine involves correcting short-term effects and long-term effects, for example. Short-term effects may be such effects as are based on short or short-term dependencies of the states of the turbine during operation. Control that is suitable for short-term effects in a turbine is known from WO 2011/110404 A1. By contrast, long-term effects may be such effects as are based on long or long-term dependencies of the states of the turbine during operation.
For the example of a gas turbine, uncontrolled or less than optimally controlled long-term effects may cause increased emissions from the gas turbine and also occurring dynamics in the gas turbine.